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  • AngularJS dealing with large data sets (Strategy)

    - by Brian
    I am working on developing a personal temperature logging viewer based on my rasppi curl'ing data into my web server's api. Temperatures are taken every 2 seconds and I can have several temperature sensors posting data. Needless to say I will have a lot of data to handle even within the scope of an hour. I have implemented a very simple paging api from the server so the server doesn't timeout and is currently only returning data in 1000 units per call, then paging through the data. I had the idea to intially show say the last 20 minutes of data from a sensor (or all sensors depending on user choices), then allowing the user to select other timeframes from which to show data. The issue comes in when you want to view all sensors or an extended time period (say 24 hours). Is there a best practice of handling this large amount of data? Would it be useful to load those first 20 minutes into the live view and then cache into local storage something like the last 24 hours? I haven't been able to find a decent idea of this in use yet even though there are a lot of ways to take this problem. I am just looking for some suggestions as to what might provide a good balance between good performance and not caching the entire data set on the client side (as beyond a week of data this might not be feasible).

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  • I need some help creating a non-binary tree (or some other data structure that will better solve my problem)

    - by EDO
    I have about ten lists of numbers and some strings. Each list has about <= 30K lines. Each line on a list has a distinct number. I need to build an efficient way of finding all the lines in each list that has the same 'control' number (or key for dB guys) and comparing what is in their string parts. I am writing this in Java. I have thought about using trees but my brain cells are about burnt now. I need some help.

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  • replacing data.frame element-wise operations with data.table (that used rowname)

    - by Harold
    So lets say I have the following data.frames: df1 <- data.frame(y = 1:10, z = rnorm(10), row.names = letters[1:10]) df2 <- data.frame(y = c(rep(2, 5), rep(5, 5)), z = rnorm(10), row.names = letters[1:10]) And perhaps the "equivalent" data.tables: dt1 <- data.table(x = rownames(df1), df1, key = 'x') dt2 <- data.table(x = rownames(df2), df2, key = 'x') If I want to do element-wise operations between df1 and df2, they look something like dfRes <- df1 / df2 And rownames() is preserved: R> head(dfRes) y z a 0.5 3.1405463 b 1.0 1.2925200 c 1.5 1.4137930 d 2.0 -0.5532855 e 2.5 -0.0998303 f 1.2 -1.6236294 My poor understanding of data.table says the same operation should look like this: dtRes <- dt1[, !'x', with = F] / dt2[, !'x', with = F] dtRes[, x := dt1[,x,]] setkey(dtRes, x) (setkey optional) Is there a more data.table-esque way of doing this? As a slightly related aside, more generally, I would have other columns such as factors in each data.table and I would like to omit those columns while doing the element-wise operations, but still have them in the result. Does this make sense? Thanks!

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  • MVC 2 Entity Framework View Model Insert

    - by cannibalcorpse
    This is driving me crazy. Hopefully my question makes sense... I'm using MVC 2 and Entity Framework 1 and am trying to insert a new record with two navigation properties. I have a SQL table, Categories, that has a lookup table CategoryTypes and another self-referencing lookup CategoryParent. EF makes two nav properties on my Category model, one called Parent and another called CategoryType, both instances of their respective models. On my view that creates the new Category, I have two dropdowns, one for the CategoryType and another for the ParentCategory. When I try and insert the new Category WITHOUT the ParentCategory, which allows nulls, everything is fine. As soon as I add the ParentCategory, the insert fails, and oddly (or so I think) complains about the CategoryType in the form of this error: 0 related 'CategoryTypes' were found. 1 'CategoryTypes' is expected. When I step through, I can verifiy that both ID properties coming in on the action method parameter are correct. I can also verify that when I go to the db to get the CategoryType and ParentCategory with the ID's, the records are being pulled fine. Yet it fails on SaveChanges(). All that I can see is that my CategoryParent dropdownlistfor in my view, is somehow causing the insert to bomb. Please see my comments in my httpPost Create action method. My view model looks like this: public class EditModel { public Category MainCategory { get; set; } public IEnumerable<CategoryType> CategoryTypesList { get; set; } public IEnumerable<Category> ParentCategoriesList { get; set; } } My Create action methods look like this: // GET: /Categories/Create public ActionResult Create() { return View(new EditModel() { CategoryTypesList = _db.CategoryTypeSet.ToList(), ParentCategoriesList = _db.CategorySet.ToList() }); } // POST: /Categories/Create [HttpPost] public ActionResult Create(Category mainCategory) { if (!ModelState.IsValid) return View(new EditModel() { MainCategory = mainCategory, CategoryTypesList = _db.CategoryTypeSet.ToList(), ParentCategoriesList = _db.CategorySet.ToList() }); mainCategory.CategoryType = _db.CategoryTypeSet.First(ct => ct.Id == mainCategory.CategoryType.Id); // This db call DOES get the correct Category, but fails on _db.SaveChanges(). // Oddly the error is related to CategoryTypes and not Category. // Entities in 'DbEntities.CategorySet' participate in the 'FK_Categories_CategoryTypes' relationship. // 0 related 'CategoryTypes' were found. 1 'CategoryTypes' is expected. //mainCategory.Parent = _db.CategorySet.First(c => c.Id == mainCategory.Parent.Id); // If I just use the literal ID of the same Category, // AND comment out the CategoryParent dropdownlistfor in the view, all is fine. mainCategory.Parent = _db.CategorySet.First(c => c.Id == 2); _db.AddToCategorySet(mainCategory); _db.SaveChanges(); return RedirectToAction("Index"); } Here is my Create form on the view : <% using (Html.BeginForm()) {%> <%= Html.ValidationSummary(true) %> <fieldset> <legend>Fields</legend> <div> <%= Html.LabelFor(model => model.MainCategory.Parent.Id) %> <%= Html.DropDownListFor(model => model.MainCategory.Parent.Id, new SelectList(Model.ParentCategoriesList, "Id", "Name")) %> <%= Html.ValidationMessageFor(model => model.MainCategory.Parent.Id) %> </div> <div> <%= Html.LabelFor(model => model.MainCategory.CategoryType.Id) %> <%= Html.DropDownListFor(model => model.MainCategory.CategoryType.Id, new SelectList(Model.CategoryTypesList, "Id", "Name"))%> <%= Html.ValidationMessageFor(model => model.MainCategory.CategoryType.Id)%> </div> <div> <%= Html.LabelFor(model => model.MainCategory.Name) %> <%= Html.TextBoxFor(model => model.MainCategory.Name)%> <%= Html.ValidationMessageFor(model => model.MainCategory.Name)%> </div> <div> <%= Html.LabelFor(model => model.MainCategory.Description)%> <%= Html.TextAreaFor(model => model.MainCategory.Description)%> <%= Html.ValidationMessageFor(model => model.MainCategory.Description)%> </div> <div> <%= Html.LabelFor(model => model.MainCategory.SeoName)%> <%= Html.TextBoxFor(model => model.MainCategory.SeoName, new { @class = "large" })%> <%= Html.ValidationMessageFor(model => model.MainCategory.SeoName)%> </div> <div> <%= Html.LabelFor(model => model.MainCategory.HasHomepage)%> <%= Html.CheckBoxFor(model => model.MainCategory.HasHomepage)%> <%= Html.ValidationMessageFor(model => model.MainCategory.HasHomepage)%> </div> <p><input type="submit" value="Create" /></p> </fieldset> <% } %> Maybe I've just been staying up too late playing with MVC 2? :) Please let me know if I'm not being clear enough.

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  • PHP - post data ends when '&' is in data.

    - by Phil Jackson
    Hi all, im posting data using jquery/ajax and PHP at the backend. Problem being, when I input something like 'Jack & Jill went up the hill' im only recieving 'Jack' when it gets to the backend. I have thrown an error at the frontend before that data is sent which alerts 'Jack & Jill went up the hill'. When I put die(print_r($_POST)); at the very top of my index page im only getting [key] => Jack how can I be loosing the data? I thought It may have been my filter; <?php function filter( $data ) { $data = trim( htmlentities( strip_tags( mb_convert_encoding( $data, 'HTML-ENTITIES', "UTF-8") ) ) ); if ( get_magic_quotes_gpc() ) { $data = stripslashes( $data ); } //$data = mysql_real_escape_string( $data ); return $data; } echo "<xmp>" . filter("you & me") . "</xmp>"; ?> but that returns fine in the test above you &amp; me which is in place after I added die(print_r($_POST));. Can anyone think of how and why this is happening? Any help much appreciated. Regards, Phil.

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  • The Next Wave of PeopleSoft Capabilities for the Staffing Industry Is Here

    - by Mark Rosenberg
    With the release of PeopleSoft Financials and Supply Chain Management 9.1 Feature Pack 2 in January this year, we introduced substantial new capabilities for our Staffing Industry customers. Through a co-development project with Infosys Limited, we have enriched Oracle's PeopleSoft Staffing Solution with new tools aimed at accelerating and improving the quality of job order fulfillment, increasing branch recruiter productivity, and driving profitable growth. Staffing industry firms succeed based on their ability to rapidly, cost-effectively, and continually fill their pipelines with new clients and job orders, recruit the best talent, and match orders with talent. Pressure to execute in each of these functional areas is even more acute on staffing firms as contingent labor becomes a more substantial and permanent part of the workforce mix. In an industry that creates value through speedy execution, there is little room for manual, inefficient processes and brittle, custom integrations, which throttle profitability and growth. The latest wave of investment in the PeopleSoft Staffing Solution focuses on generating efficiency and flexibility for our customers. Simplicity To operate profitably and continue growing, a Staffing enterprise needs its client management, recruiting, order fulfillment, and other processes to function in harmony. Most importantly, they need to be simple for recruiters, branch managers, and applicants to access and understand. The latest PeopleSoft Staffing Solution set of enhancements includes numerous automated defaulting mechanisms and information-rich dashboard pagelets that even a new employee can learn quickly. Pending Applicant, Agenda management, Search, and other pagelets are just a few of the newest, easy-to-use tools that not only aggregate and summarize information, but also provide instant access to applicants, tasks, and key reports for branch staff. Productivity The leading firms in the Staffing industry are those that can more efficiently orchestrate large numbers of candidates, clients, and orders than their competitors can. PeopleSoft Financials and Supply Chain Management 9.1 Feature Pack 2 delivers productivity boosters that Staffing firms can leverage to streamline tasks and processes for competitive advantage. For example, we enhanced the Recruiting Funnel, which manages the candidate on-boarding process, with a highly interactive user interface. It integrates disparate Staffing business processes and exploits new PeopleTools technologies to offer a superior on-boarding user experience. Automated creation of agenda items and assignment tasks for each candidate minimizes setup and organizes assignment steps for the on-boarding process. Mass updates of tasks and instant access to the candidate overview page (which we also expanded), candidate event status, event counts, and other key data enable recruiters to better serve clients and candidates. Lower TCO Constructing and maintaining an efficient yet flexible labor supply chain can be complicated, let alone expensive. Traditionally, Staffing firms have been challenged in controlling their technology cost of ownership because connecting candidate and client-facing tools involved building and integrating custom applications and technologies and managing staff turnover, placing heavy demands on IT and support staff. With PeopleSoft Financials and Supply Chain Management 9.1 Feature Pack 2, there are two major enhancements that aggressively tackle these challenges. First, we added another integration framework to enable cost-effective linking of the Staffing firm’s PeopleSoft applications and its job board distributors. (The first PeopleSoft 9.1 Feature Pack released in March 2011 delivered an integration framework to connect to resume parsing providers.) Second, we introduced the teaming concept to enable work to be partitioned to groups, as well as individuals. These two capabilities, combined with a host of others, position Staffing firms to configure and grow their businesses without growing their IT and overhead expenditures. For our Staffing Industry customers, PeopleSoft Financials and Supply Chain Management 9.1 Feature Pack 2 is loaded with high-value tools aimed at enabling and sustaining a flexible labor supply chain. For more information, contact [email protected] or [email protected].

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  • SQL Developer Debugging, Watches, Smart Data, & Data

    - by thatjeffsmith
    After presenting the SQL Developer PL/SQL debugger for about an hour yesterday at KScope12 in San Antonio, my boss came up and asked, “Now, would you really want to know what the Smart Data panel does?” Apparently I had ‘made up’ my own story about what that panel’s intent is based on my experience with it. Not good Jeff, not good. It was a very small point of my presentation, but I probably should have read the docs. The Smart Data tab displays information about variables, using your Debugger: Smart Data preferences. You can also specify these preferences by right-clicking in the Smart Data window and selecting Preferences. Debugger Smart Data Preferences, control number of variables to display The Smart Data panel auto-inspects the last X accessed variables. So if you have a program with 26 variables, instead of showing you all 26, it will just show you the last two variables that were referenced in your program. If you were to click on the ‘Data’ debug panel, you’ll see EVERYTHING. And if you only want to see a very specific set of values, then you should use Watches. The Smart Data Panel As I step through the code, the variables being tracked change as they are referenced. Only the most recent ones display. This is controlled by the ‘Maximum Locations to Remember’ preference. Step through the code, see the latest variables accessed The Data Panel All variables are displayed. Might be information overload on large PL/SQL programs where you have many dozens or even hundreds of variables to track. Shows everything all the time Watches Watches are added manually and only show what you ask for. Data on Demand – add a watch to track a specific variable Remember, you can interact with your data If you want to do more than just watch, you can mouse-right on a data element, and change the value of the variable as the program is running. This is one of the primary benefits to debugging over using DBMS_OUTPUT to track what’s happening in your program. Change the values while the program is running to test your ‘What if?’ scenarios

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  • SQL – Biggest Concerns in a Data-Driven World

    - by Pinal Dave
    The ongoing chaos over Government Agency’s snooping has ignited a heated debate on privacy of personal data and its use by government and/or other institutions. It has created a feeling of disapproval and distrust among users. This incident proves to be a lesson for companies that are looking to leverage their business using a data driven approach. According to analysts, the goal of gathering personal information should be to deliver benefits to both the parties – the user as well as the data collector(government or business). Using data the right way is crucial, and companies need to deploy the right software applications and systems to ensure that their efforts are well-directed. However, there are various issues plaguing analysts regarding available software, which are highlighted below. According to a InformationWeek 2013 Survey of Analytics, Business Intelligence and Information Management where 541 business technology professionals contributed as respondents, it was discovered that the biggest concern was deemed to be the scarcity of expertise and high costs associated with the same. This concern was voiced by as many as 38% of the participants. A close second came out to be the issue of data warehouse appliance platforms being expensive, with 33% of those present believing it to be a huge roadblock. Another revelation made in this respect was that 31% professionals weren’t even sure how Data Analytics can create business opportunities for them. Another 17% shared that they found data platform technologies such as Hadoop and NoSQL technologies hard to learn. These results clearly pointed out that there are awareness and expertise issues that also need much attention. Unless the demand-supply gap of Business Intelligence professionals well versed in data analysis technologies is met, this divide is going to affect how companies make the most of their BI campaigns. One of the key action points that can be taken to salvage the situation, is to provide training on Data Analytics concepts. Koenig Solutions offer courses on many such technologies including a course on MCSE SQL Server 2012: BI Platform. So it’s time to brush up your skills and get down to work in a data driven world that awaits you ahead. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Store XML data in Core Data

    - by ct2k7
    Hi, is there any easy way of store XML data into core data? Currently, my app just pulls the values from the XML file directly, however, this isn't efficient for XML files which holds over 100 entries, thus storing the data in Core Data would be the best option. XML file is called/downloaded/parsed ever time the app opens. With the Core Data, the XML data would be downloaded ever 3600 seconds or so, and refresh the current data in the core data, to reduce the loading time when opening the app. Any ideas on how I can do this? Having reviewed the developer documentation, it doesn't look very tasty.

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  • Where can I find free and open data?

    - by kitsune
    Sooner or later, coders will feel the need to have access to "open data" in one of their projects, from knowing a city's zip to a more obscure information such as the axial tilt of Pluto. I know data.un.org which offers access to the UN's extensive array of databases that deal with human development and other socio-economic issues. The other usual suspects are NASA and the USGS for planetary data. There's an article at readwriteweb with more links. infochimps.org seems to stand out. Personally, I need to find historic commodity prices, stock values and other financial data. All these data sets seem to cost money however. Clarification To clarify, I'm interested in all kinds of open data, because sooner or later, I know I will be in a situation where I could need it. I will try to edit this answer and include the suggestions in a structured manners. A link for financial data was hidden in that readwriteweb article, doh! It's called opentick.com. Looks good so far! Update I stumbled over semantic data in another question of mine on here. There is opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). A project called UMBEL provides a light-weight, distilled version of opencyc. Umbel has semantic data in rdf/owl/skos n3 syntax. The Worldbank also released a very nice API. It offers data from the last 50 years for about 200 countries

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  • Generate Entity Data Model from Data Contract

    - by CSmooth.net
    I would like to find a fast way to convert a Data Contract to a Entity Data Model. Consider the following Data Contract: [DataContract] class PigeonHouse { [DataMember] public string housename; [DataMember] public List<Pigeon> pigeons; } [DataContract] class Pigeon { [DataMember] public string name; [DataMember] public int numberOfWings; [DataMember] public int age; } Is there an easy way to create an ADO.NET Entity Data Model from this code?

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  • Referencing Entity from external data model - Core Data

    - by Ben Reeves
    I have a external library which includes a core data model, I would like to add a new entity to this model which has a relationship with one of the entities from the library. I know I could modify the original, but is there a way to without needing to pollute the library? I tried just creating a new model with an entity named the same, but that doesn't work: * Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: 'Can't merge models with two different entities named 'Host''

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  • Temporary storage for keeping data between program iterations?

    - by mr.b
    I am working on an application that works like this: It fetches data from many sources, resulting in pool of about 500,000-1,500,000 records (depends on time/day) Data is parsed Part of data is processed in a way to compare it to pre-existing data (read from database), calculations are made, and stored in database. Resulting dataset that has to be stored in database is, however, much smaller in size (compared to original data set), and ranges from 5,000-50,000 records. This process almost always updates existing data, perhaps adds few more records. Then, data from step 2 should be kept somehow, somewhere, so that next time data is fetched, there is a data set which can be used to perform calculations, without touching pre-existing data in database. I should point out that this data can be lost, it's not irreplaceable (key information can be read from database if needed), but it would speed up the process next time. Application components can (and will be) run off different computers (in the same network), so storage has to be reachable from multiple hosts. I have considered using memcached, but I'm not quite sure should I do so, because one record is usually no smaller than 200 bytes, and if I have 1,500,000 records, I guess that it would amount to over 300 MB of memcached cache... But that doesn't seem scalable to me - what if data was 5x that amount? If it were to consume 1-2 GB of cache only to keep data in between iterations (which could easily happen)? So, the question is: which temporary storage mechanism would be most suitable for this kind of processing? I haven't considered using mysql temporary tables, as I'm not sure if they can persist between sessions, and be used by other hosts in network... Any other suggestion? Something I should consider?

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  • Core Data => Adding a related object always nil

    - by mongeta
    Hello, I have two tables related: DataEntered and Model DataEntered -currentModel Model One DataEntered can have only ONE Model, but a Model can stay into many DataEntered. The relationship is from DataEntered to Model (No To Many-relathionship) and no inverse relation. XCode generates the setters for DataEnteredModel: @property (nonatomic, retain) NSSet * current_model; - (void)addCurrent_modelObject:(CarModel *)value; - (void)addCurrent_model:(NSSet *)value; I have a Table and when I select a model, I want to store it to DataEntered: Model *model = [fetchedResultsController objectAtIndexPath:indexPath]; NSLog(@"Model %@",model.name); // ==> gives me the correct model name [dataEntered addCurrent_modelObject:model]; // ==> always nil [dataEntered setCurrent_model:[fetchedResultsController objectAtIndexPath:indexPath]]; // the same, always nil what I'm doing wrong ????? thanks, r.

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  • Passing variables from Model to Model in codeigniter

    - by Craig Ward
    Hi, I need to pass a variable to model, that model needs to send another back and use that variable to query a different model. EG: I have a product_ID which I send to the product model, From that I find out the supplier_ID. I want to grab that supplier_ID to the supplier model to get the supplier name. How do you implement this in codeigniter?

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  • Why are data structures so important in interviews?

    - by Vamsi Emani
    I am a newbie into the corporate world recently graduated in computers. I am a java/groovy developer. I am a quick learner and I can learn new frameworks, APIs or even programming languages within considerably short amount of time. Albeit that, I must confess that I was not so strong in data structures when I graduated out of college. Through out the campus placements during my graduation, I've witnessed that most of the biggie tech companies like Amazon, Microsoft etc focused mainly on data structures. It appears as if data structures is the only thing that they expect from a graduate. Adding to this, I see that there is this general perspective that a good programmer is necessarily a one with good knowledge about data structures. To be honest, I felt bad about that. I write good code. I follow standard design patterns of coding, I do use data structures but at the superficial level as in java exposed APIs like ArrayLists, LinkedLists etc. But the companies usually focused on the intricate aspects of Data Structures like pointer based memory manipulation and time complexities. Probably because of my java-ish background, Back then, I understood code efficiency and logic only when talked in terms of Object Oriented Programming like Objects, instances, etc but I never drilled down into the level of bits and bytes. I did not want people to look down upon me for this knowledge deficit of mine in Data Structures. So really why all this emphasis on Data Structures? Does, Not having knowledge in Data Structures really effect one's career in programming? Or is the knowledge in this subject really a sufficient basis to differentiate a good and a bad programmer?

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  • Data structure for pattern matching.

    - by alvonellos
    Let's say you have an input file with many entries like these: date, ticker, open, high, low, close, <and some other values> And you want to execute a pattern matching routine on the entries(rows) in that file, using a candlestick pattern, for example. (See, Doji) And that pattern can appear on any uniform time interval (let t = 1s, 5s, 10s, 1d, 7d, 2w, 2y, and so on...). Say a pattern matching routine can take an arbitrary number of rows to perform an analysis and contain an arbitrary number of subpatterns. In other words, some patterns may require 4 entries to operate on. Say also that the routine (may) later have to find and classify extrema (local and global maxima and minima as well as inflection points) for the ticker over a closed interval, for example, you could say that a cubic function (x^3) has the extrema on the interval [-1, 1]. (See link) What would be the most natural choice in terms of a data structure? What about an interface that conforms a Ticker object containing one row of data to a collection of Ticker so that an arbitrary pattern can be applied to the data. What's the first thing that comes to mind? I chose a doubly-linked circular linked list that has the following methods: push_front() push_back() pop_front() pop_back() [] //overloaded, can be used with negative parameters But that data structure seems very clumsy, since so much pushing and popping is going on, I have to make a deep copy of the data structure before running an analysis on it. So, I don't know if I made my question very clear -- but the main points are: What kind of data structures should be considered when analyzing sequential data points to conform to a pattern that does NOT require random access? What kind of data structures should be considered when classifying extrema of a set of data points?

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  • What development terms come from the construction industry?

    - by Renesis
    When talking with colleagues about software design and development principles, I've noticed one of the most common sources for analogies is the construction industry. It would be quite useful to have a list of what programming/development terms and concepts are borrowed from construction industry? (And please provide a description or experiences regarding that topic.) [Credit to Programming concepts taken from the arts and humanities for the idea]

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  • Willy Rotstein at the Oracle Retail Industry Forum

    - by user801960
    In the video below, Willy Rotstein from Oracle Retail talks about the success of the Oracle Retail Industry Forum that took place in Berlin in October.  Willy talks about the main discussion topics at the forum and summarises the thoughts of the retailers that attended the forum.  The forum was an excellent way for retailers to come together and discuss the challenges of the industry and share experiences in developing solutions to those challenges.  

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  • Five Key Strategies in Master Data Management

    - by david.butler(at)oracle.com
    Here is a very interesting Profit Magazine article on MDM: A recent customer survey reveals the deleterious effects of data fragmentation. by Trevor Naidoo, December 2010   Across industries and geographies, IT organizations have grown in complexity, whether due to mergers and acquisitions, or decentralized systems supporting functional or departmental requirements. With systems architected over time to support unique, one-off process needs, they are becoming costly to maintain, and the Internet has only further added to the complexity. Data fragmentation has become a key inhibitor in delivering flexible, user-friendly systems. The Oracle Insight team conducted a survey assessing customers' master data management (MDM) capabilities over the past two years to get a sense of where they are in terms of their capabilities. The responses, by 27 respondents from six different industries, reveal five key areas in which customers need to improve their data management in order to get better financial results. 1. Less than 15 percent of organizations surveyed understand the sources and quality of their master data, and have a roadmap to address missing data domains. Examples of the types of master data domains referred to are customer, supplier, product, financial and site. Many organizations have multiple sources of master data with varying degrees of data quality in each source -- customer data stored in the customer relationship management system is inconsistent with customer data stored in the order management system. Imagine not knowing how many places you stored your customer information, and whether a customer's address was the most up to date in each source. In fact, more than 55 percent of the respondents in the survey manage their data quality on an ad-hoc basis. It is important for organizations to document their inventory of data sources and then profile these data sources to ensure that there is a consistent definition of key data entities throughout the organization. Some questions to ask are: How do we define a customer? What is a product? How do we define a site? The goal is to strive for one common repository for master data that acts as a cross reference for all other sources and ensures consistent, high-quality master data throughout the organization. 2. Only 18 percent of respondents have an enterprise data management strategy to ensure that data is treated as an asset to the organization. Most respondents handle data at the department or functional level and do not have an enterprise view of their master data. The sales department may track all their interactions with customers as they move through the sales cycle, the service department is tracking their interactions with the same customers independently, and the finance department also has a different perspective on the same customer. The salesperson may not be aware that the customer she is trying to sell to is experiencing issues with existing products purchased, or that the customer is behind on previous invoices. The lack of a data strategy makes it difficult for business users to turn data into information via reports. Without the key building blocks in place, it is difficult to create key linkages between customer, product, site, supplier and financial data. These linkages make it possible to understand patterns. A well-defined data management strategy is aligned to the business strategy and helps create the governance needed to ensure that data stewardship is in place and data integrity is intact. 3. Almost 60 percent of respondents have no strategy to integrate data across operational applications. Many respondents have several disparate sources of data with no strategy to keep them in sync with each other. Even though there is no clear strategy to integrate the data (see #2 above), the data needs to be synced and cross-referenced to keep the business processes running. About 55 percent of respondents said they perform this integration on an ad hoc basis, and in many cases, it is done manually with the help of Microsoft Excel spreadsheets. For example, a salesperson needs a report on global sales for a specific product, but the product has different product numbers in different countries. Typically, an analyst will pull all the data into Excel, manually create a cross reference for that product, and then aggregate the sales. The exact same procedure has to be followed if the same report is needed the following month. A well-defined consolidation strategy will ensure that a central cross-reference is maintained with updates in any one application being propagated to all the other systems, so that data is synchronized and up to date. This can be done in real time or in batch mode using integration technology. 4. Approximately 50 percent of respondents spend manual efforts cleansing and normalizing data. Information stored in various systems usually follows different standards and formats, making it difficult to match the data. A customer's address can be stored in different ways using a variety of abbreviations -- for example, "av" or "ave" for avenue. Similarly, a product's attributes can be stored in a number of different ways; for example, a size attribute can be stored in inches and can also be entered as "'' ". These types of variations make it difficult to match up data from different sources. Today, most customers rely on manual, heroic efforts to match, cleanse, and de-duplicate data -- clearly not a scalable, sustainable model. To solve this challenge, organizations need the ability to standardize data for customers, products, sites, suppliers and financial accounts; however, less than 10 percent of respondents have technology in place to automatically resolve duplicates. It is no wonder, therefore, that we get communications about products we don't own, at addresses we don't reside, and using channels (like direct mail) we don't like. An all-too-common example of a potential challenge follows: Customers end up receiving duplicate communications, which not only impacts customer satisfaction, but also incurs additional mailing costs. Cleansing, normalizing, and standardizing data will help address most of these issues. 5. Only 10 percent of respondents have the ability to share data that was mastered in a master data hub. Close to 60 percent of respondents have efforts in place that profile, standardize and cleanse data manually, and the output of these efforts are stored in spreadsheets in various parts of the organization. This valuable information is not easily shared with the rest of the organization and, more importantly, this enriched information cannot be sent back to the source systems so that the data is fixed at the source. A key benefit of a master data management strategy is not only to clean the data, but to also share the data back to the source systems as well as other systems that need the information. Aside from the source systems, another key beneficiary of this data is the business intelligence system. Having clean master data as input to business intelligence systems provides more accurate and enhanced reporting.  Characteristics of Stellar MDM When deciding on the right master data management technology, organizations should look for solutions that have four main characteristics: enterprise-grade MDM performance complete technology that can be rapidly deployed and addresses multiple business issues end-to-end MDM process management with data quality monitoring and assurance pre-built MDM business relevant applications with data stores and workflows These master data management capabilities will aid in moving closer to a best-practice maturity level, delivering tremendous efficiencies and savings as well as revenue growth opportunities as a result of better understanding your customers.  Trevor Naidoo is a senior director in Industry Strategy and Insight at Oracle. 

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  • Address Regulatory Mandates for Data Encryption Without Changing Your Applications

    - by Troy Kitch
    The Payment Card Industry Data Security Standard, US state-level data breach laws, and numerous data privacy regulations worldwide all call for data encryption to protect personally identifiable information (PII). However encrypting PII data in applications requires costly and complex application changes. Fortunately, since this data typically resides in the application database, using Oracle Advanced Security, PII can be encrypted transparently by the Oracle database without any application changes. In this ISACA webinar, learn how Oracle Advanced Security offers complete encryption for data at rest, in transit, and on backups, along with built-in key management to help organizations meet regulatory requirements and save money. You will also hear from TransUnion Interactive, the consumer subsidiary of TransUnion, a global leader in credit and information management, which maintains credit histories on an estimated 500 million consumers across the globe, about how they addressed PCI DSS encryption requirements using Oracle Database 11g with Oracle Advanced Security. Register to watch the webinar now.

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  • Using Hadooop (HDInsight) with Microsoft - Two (OK, Three) Options

    - by BuckWoody
    Microsoft has many tools for “Big Data”. In fact, you need many tools – there’s no product called “Big Data Solution” in a shrink-wrapped box – if you find one, you probably shouldn’t buy it. It’s tempting to want a single tool that handles everything in a problem domain, but with large, complex data, that isn’t a reality. You’ll mix and match several systems, open and closed source, to solve a given problem. But there are tools that help with handling data at large, complex scales. Normally the best way to do this is to break up the data into parts, and then put the calculation engines for that chunk of data right on the node where the data is stored. These systems are in a family called “Distributed File and Compute”. Microsoft has a couple of these, including the High Performance Computing edition of Windows Server. Recently we partnered with Hortonworks to bring the Apache Foundation’s release of Hadoop to Windows. And as it turns out, there are actually two (technically three) ways you can use it. (There’s a more detailed set of information here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx, I’ll cover the options at a general level below)  First Option: Windows Azure HDInsight Service  Your first option is that you can simply log on to a Hadoop control node and begin to run Pig or Hive statements against data that you have stored in Windows Azure. There’s nothing to set up (although you can configure things where needed), and you can send the commands, get the output of the job(s), and stop using the service when you are done – and repeat the process later if you wish. (There are also connectors to run jobs from Microsoft Excel, but that’s another post)   This option is useful when you have a periodic burst of work for a Hadoop workload, or the data collection has been happening into Windows Azure storage anyway. That might be from a web application, the logs from a web application, telemetrics (remote sensor input), and other modes of constant collection.   You can read more about this option here:  http://blogs.msdn.com/b/windowsazure/archive/2012/10/24/getting-started-with-windows-azure-hdinsight-service.aspx Second Option: Microsoft HDInsight Server Your second option is to use the Hadoop Distribution for on-premises Windows called Microsoft HDInsight Server. You set up the Name Node(s), Job Tracker(s), and Data Node(s), among other components, and you have control over the entire ecostructure.   This option is useful if you want to  have complete control over the system, leave it running all the time, or you have a huge quantity of data that you have to bulk-load constantly – something that isn’t going to be practical with a network transfer or disk-mailing scheme. You can read more about this option here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx Third Option (unsupported): Installation on Windows Azure Virtual Machines  Although unsupported, you could simply use a Windows Azure Virtual Machine (we support both Windows and Linux servers) and install Hadoop yourself – it’s open-source, so there’s nothing preventing you from doing that.   Aside from being unsupported, there are other issues you’ll run into with this approach – primarily involving performance and the amount of configuration you’ll need to do to access the data nodes properly. But for a single-node installation (where all components run on one system) such as learning, demos, training and the like, this isn’t a bad option. Did I mention that’s unsupported? :) You can learn more about Windows Azure Virtual Machines here: http://www.windowsazure.com/en-us/home/scenarios/virtual-machines/ And more about Hadoop and the installation/configuration (on Linux) here: http://en.wikipedia.org/wiki/Apache_Hadoop And more about the HDInsight installation here: http://www.microsoft.com/web/gallery/install.aspx?appid=HDINSIGHT-PREVIEW Choosing the right option Since you have two or three routes you can go, the best thing to do is evaluate the need you have, and place the workload where it makes the most sense.  My suggestion is to install the HDInsight Server locally on a test system, and play around with it. Read up on the best ways to use Hadoop for a given workload, understand the parts, write a little Pig and Hive, and get your feet wet. Then sign up for a test account on HDInsight Service, and see how that leverages what you know. If you're a true tinkerer, go ahead and try the VM route as well. Oh - there’s another great reference on the Windows Azure HDInsight that just came out, here: http://blogs.msdn.com/b/brunoterkaly/archive/2012/11/16/hadoop-on-azure-introduction.aspx  

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  • Oracle - A Leader in Gartner's MQ for Master Data Management for Customer Data

    - by Mala Narasimharajan
      The Gartner MQ report for Master Data Management of Customer Data Solutions is released and we're proud to say that Oracle is in the leaders' quadrant.  Here's a snippet from the report itself:  " “Oracle has a strong, though complex, portfolio of domain-specific MDM products that include prepackaged data models. Gartner estimates that Oracle now has over 1,500 licensed MDM customers, including 650 customers managing customer data. The MDM portfolio includes three products that address MDM of customer data solution needs: Oracle Fusion Customer Hub (FCH), Oracle CDH and Oracle Siebel UCM. These three MDM products are positioned for different segments of the market and Oracle is progressively moving all three products onto a common MDM technology platform..." (Gartner, Oct 18, 2012)  For more information on Oracle's solutions for customer data in Master Data Management, click here.  

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  • Almost Realtime Data and Web application

    - by Chris G.
    I have a computer that is recording 100 different data points into an OPC server. I've written a simple OPC client that can read all of this data. I have a front-end website on a different network that I would like to consume this data. I could easily set the OPC client to send the data to a SQL server and the website could read from it, but that would be a lot of writes. If I wanted the data to be updated every 10 seconds I'd be writing to the database every 10 seconds. (I could probably just serialize the 100 points to get 1 write / 10 seconds but that would also limit my ability to search the data later). This solution wouldn't scale very well. If I had 100 of these computers the situation would quickly grow out of hand. Obviously I am well out of my league here and I have no experience with working with a large amount of data like this. What are my options and what should I research?

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